Factorization of the dijet cross section in hadron-hadron collisions

TL;DR

This paper establishes a factorization theorem for dijet cross sections in hadron-hadron collisions using effective theory, addressing divergences and resumming large logarithms for improved theoretical predictions.

Contribution

It provides a detailed factorization framework employing soft-collinear effective theory, including divergence cancellation and resummation techniques for dijet processes with cone-type algorithms.

Findings

All infrared and rapidity divergences cancel out.

Computed anomalous dimensions for factorized components.

Resummed large logarithms to next-to-leading order accuracy.

Abstract

The factorization theorem for the dijet cross section is presented in hadron-hadron collisions with a cone-type jet algorithm. We also apply the beam veto to the beam jets consisting of the initial radiation. The soft-collinear effective theory is employed to see the factorization structure transparently when there are four distinct lightcone directions involved. There are various types of divergences such as the ultraviolet and infrared divergences. And when the phase space is divided to probe the collinear and the soft parts, there appears an additional divergence called rapidity divergence. These divergences are sorted out and we will show that all the infrared and rapidity divergences cancel, and only the ultraviolet divergence remains. It is a vital step to justify the factorization. Among many partonic processes, we take $q\overline{q} \rightarrow gg$ as a specific example to consider the dijet cross section. The hard and the soft functions have nontrivial color structure, while the jet and the beam functions are diagonal in operator basis. The dependence of the soft anomalous dimension on the jet algorithm and the beam veto is diagonal in operator space, and is cancelled by that of the jet and beam functions. We also compute the anomalous dimensions of the factorized components, and resum the large logarithms to next-to-leading logarithmic accuracy by solving the renormalization group equation.